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    Graphical structure model-based transaction risk control
    3.
    发明授权
    Graphical structure model-based transaction risk control 有权

    公开(公告)号：US11526766B2

    公开(公告)日：2022-12-13

    申请号：US16805387

    申请日：2020-02-28

    Applicant: Advanced New Technologies Co., Ltd.

    Inventor： Le Song ,  Hui Li ,  Zhibang Ge ,  Xin Huang ,  Chunyang Wen ,  Lin Wang ,  Tao Jiang ,  Yiguang Wang ,  Xiaofu Chang ,  Guanyin Zhu

    IPC: G06N3/08 ,  G06F16/23 ,  G06N3/04 ,  G06Q20/40 ,  G06Q40/02

    Abstract: One or more implementations of the present specification provide risk control of transactions based on a graphical structure model. A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on a transaction data network that includes nodes representing entities in a transaction and edges representing relationships between the entities. Each labeled sample includes a label indicating whether a node corresponding to the labeled sample is a risky transaction node. The graphical structure model is configured to iteratively calculate an embedding vector of the node in a latent feature space based on an original feature of the node or a feature of an edge associated with the node. An embedding vector of an input sample is calculated by using the graphical structure model. Transaction risk control is performed on the input sample based on the embedding vector.

    CHIP AND CHIP-BASED DATA PROCESSING METHOD
    4.
    发明申请
    CHIP AND CHIP-BASED DATA PROCESSING METHOD 有权

    公开(公告)号：US20210049453A1

    公开(公告)日：2021-02-18

    申请号：US17084936

    申请日：2020-10-30

    Applicant: Advanced New Technologies Co., Ltd.

    Inventor： Guozhen Pan ,  Jianguo Xu ,  Yongchao Liu ,  Haitao Zhang ,  Qiyin Huang ,  Guanyin Zhu

    IPC: G06N3/063 ,  G06F13/28 ,  G06F9/38 ,  G06F9/30

    Abstract: Embodiments of the present specification provide chips and chip-based data processing methods. In an embodiment, a method comprises: obtaining data associated with one or more neural networks transmitted from a server; for each layer of a neural network of the one or more neural networks, configuring, based on the data, a plurality of operator units based on a type of computation each operator unit performs; and invoking the plurality of operator units to perform computations, based on neurons of a layer of the neural network immediately above, of the data for each neuron to produce a value of the neuron.

    Graphical structure model-based credit risk control
    8.
    发明授权
    Graphical structure model-based credit risk control 有权

    公开(公告)号：US11526936B2

    公开(公告)日：2022-12-13

    申请号：US16805538

    申请日：2020-02-28

    Applicant: Advanced New Technologies Co., Ltd.

    Inventor： Le Song ,  Hui Li ,  Zhibang Ge ,  Xin Huang ,  Chunyang Wen ,  Lin Wang ,  Tao Jiang ,  Yiguang Wang ,  Xiaofu Chang ,  Guanyin Zhu

    IPC: G06Q40/02 ,  G06N20/10 ,  G06K9/62 ,  G06N7/00

    Abstract: A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on an enterprise relationship network that includes nodes and edges. Each labeled sample includes a label indicating whether a corresponding node is a risky credit node. The graphical structure model is configured to iteratively calculate an embedding vector of at least one node in a hidden feature space based on an original feature of the at least one node and/or a feature of an edge associated with the at least one node. An embedding vector corresponding to a test-sample is calculated by using the graphical structure model. Credit risk analysis is performed on the test-sample. The credit risk analysis is performed based on a feature of the test-sample represented in the embedding vector. A node corresponding to the test-sample is labeled as a credit risk node.

    CHIP AND CHIP-BASED DATA PROCESSING METHOD
    9.
    发明申请
    CHIP AND CHIP-BASED DATA PROCESSING METHOD 有权

    公开(公告)号：US20210342680A1

    公开(公告)日：2021-11-04

    申请号：US17373384

    申请日：2021-07-12

    Applicant: Advanced New Technologies Co., Ltd.

    Inventor： Guozhen Pan ,  Jianguo Xu ,  Yongchao Liu ,  Haitao Zhang ,  Qiyin Huang ,  Guanyin Zhu

    IPC: G06N3/063 ,  G06F9/30 ,  G06F9/38 ,  G06F13/28

    Abstract: Embodiments of the present specification provide chips and chip-based data processing methods. In an embodiment, a method comprises: obtaining data associated with one or more neural networks transmitted from a server; for each layer of a neural network of the one or more neural networks, configuring, based on the data, a plurality of operator units based on a type of computation each operator unit performs; and invoking the plurality of operator units to perform computations, based on neurons of a layer of the neural network immediately above, of the data for each neuron to produce a value of the neuron.

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